Samuel Chef

Frequency mapping in dynamic light emission with wavelet transform

Abstract Dynamic photon emission microscopy is an efficient tool to analyse today’s integrated circuit. Nevertheless, the reduction of transistor’s dimensions leads to more complex acquisitions where many spots can be seen. A frequency characterization of the whole acquired area can help to have a better understanding of it. With that purpose in mind, a new methodology to draw frequency mapping of dynamic light emission acquisition is reported. It is fully automated and based on wavelet transform and autocorrelation function. Regarding the possible use in an industrial context, the suggested method can help to localize abnormal emission activity and it gives some perspectives on automatic databases comparison.

Optical probing (EOFM / TRI): A large set of complementary applications for ultimate VLSI

Electro Optical Techniques (EOFM: Electro Optical Frequency Mapping and EOP: Electro Optical Probing) and Dynamic Light Emission Techniques (TRE: Time Resolved Emission and TRI: Time Resolved Imaging) are dynamic optical probing techniques widely used at IC level for design debug and defect localization purpose. They can pinpoint the origin of timing issue or logic fault in up to date CMOS devices. Each technique has its advantages and its drawbacks allowing a common set of applications and more specific ones. We have been involved in the development of the most advanced techniques related to EOFM and TRI on various devices (down to 28nm technology). What we can expect with each technique, which one to choose, what are the limitations are questions that must be answered regarding tooling cost and skills involved. Based on the understanding of the bases of each technique, their complementarities and their limitations have been identified. Even if these techniques can solve most of the issues we encountered, we can wonder if they can be applied on future technologies and this aspect will also be discussed.

Unsupervised image processing scheme for transistor photon emission analysis in order to identify defect location

Abstract. The study of the light emitted by transistors in a highly scaled complementary metal oxide semiconductor (CMOS) integrated circuit (IC) has become a key method with which to analyze faulty devices, track the failure root cause, and have candidate locations for where to start the physical analysis. The localization of defective areas in IC corresponds to a reliability check and gives information to the designer to improve the IC design. The scaling of CMOS leads to an increase in the number of active nodes inside the acquisition area. There are also more differences between the spot’s intensities. In order to improve the identification of all of the photon emission spots, we introduce an unsupervised processing scheme. It is based on iterative thresholding decomposition (ITD) and mathematical morphology operations. It unveils all of the emission spots and removes most of the noise from the database thanks to a succession of image processing. The ITD approach based on five thresholding methods is tested on 15 photon emission databases (10 real cases and 5 simulated cases). The photon emission areas’ localization is compared to an expert identification and the estimation quality is quantified using the object consistency error.

New statistical post processing approach for precise fault and defect localization in TRI database acquired on complex VLSI

Timing issue, missing or extra state transitions or unusual consumption can be detected and localized by Time Resolved Imaging (TRI) database analysis. Although, long test pattern can challenge this process. The number of photons to process rapidly increases and the acquisition time to have a good signal over noise ratio (SNR) can be prohibitive. As a result, the tracking of the defect emission signature inside a huge database can be quite complicated. In this paper, a method based on data mining techniques is suggested to help the TRI end user to have a good idea about where to start a deeper analysis of the integrated circuit, even with such complex databases.

Cluster matching in time resolved imaging for VLSI analysis

If scaling has the benefit of enabling manufacturers to design tomorrows integrated circuits, from the failure analyst point of view it also has the drawback of making devices more complex. The test sequence for modern VLSI can be quite long, with thousands of vector. Dynamic photon emission databases can contain millions of photons representing thousands of state changes in the region of interest. Finding a candidate location where to perform physical analysis is quite challenging, especially if the fault occurs on a single vector. In this paper, we suggest a new methodology to find single vector fault in dynamic photon emission database. The process is applied at the post-acquisition level and is based on clustering algorithm and nearest neighbor research.

Pattern image enhancement by extended depth of field

Abstract Most optical defect localization techniques such as dynamic laser stimulation or photon emission microscopy require a pattern image of the device to be taken. The main purpose is for device navigation, but it also enables the analyst to identify the location of the monitored activity by superimposing it onto the pattern image. The defect localization workflow usually starts at low or medium magnification. At these scales, several factors can lead to a lack of orthogonality of the sample with the optical axis of the system. Therefore, images can be locally out of focus and poorly resolved. In this paper, a method based on Depth of Field Extension is suggested to correct the pattern image.

Spatial correction in dynamic photon emission by affine transformation matrix estimation

Photon emission microscopy and Time Resolved Imaging have proved their efficiency for defect localization on VLSI. A common process to find defect candidate locations is to draw a comparison between acquisitions on a normally working device and a faulty one. In order to be accurate and meaningful, this method requires that the acquisition scene remains the same between the two parts. In practice, it can be difficult to set. In this paper, a method to correct position by affine matrix transformation is suggested. It is based on image features detection, description and matching and affine transformation estimation.

Automatic localization of signal sources in photon emission images for integrated circuit analysis

Defects localization is a key step in failure analysis of highly scaled complementary oxide semiconductor integrated circuits (ICs). It gives prior information on VLSI circuits and allows the designers to improve their diagnostic. Light emission techniques are efficient to localize defects in modern ICs. The identification of the emission spots is an essential step of the process because it shows where is located the electrical activity in the chip. Due to scaling, more and more emission nodes are located within the acquisition area so that large variations of emission intensity can exist. Thresholding techniques have been implemented, but they fail to provide an exhaustive localization. To overcome this problem, we introduce in this paper an automatic unsupervised process. It is based on a combination of median filtering, mathematical morphology and local maxima research. This new approach is evaluated and tested on 20 photon emission images (real and simulated). The final result is compared to an expert evaluation, and the detection quality is quantified.

Extensive Laser Fault Injection Profiling of 65 nm FPGA

Fault injection attacks have been widely investigated in both academia and industry during the past decade. In this attack approach, the adversary intentionally induces computational faults in the security components of the integrated circuit (IC) for deducing the confidential information processed or stored inside the device. However, the internal architecture of real-world devices is typically unknown to the attacker and the insufficient information about the device internals often cannot satisfy requirements of a practical fault injection attack. In this paper, we target Field Programmable Gate Array (FPGA) that is widely used in hardware security applications. By analyzing the faulty outputs of implemented algorithms, the scale of logic arrays and the sensitive logic cells can be precisely profiled. Using the outcome of this work, practical attacks can be significantly accelerated, without a need of time-consuming chip-scale injection scan. In addition, the observed fault models are compatible with most of the previously proposed fault models for differential or algebraic fault attacks (DFA/AFA). Moreover, a low-cost and highly sensitive logic-level countermeasure for predicting the laser fault injection attempt is described, which can be applied into any digital IC with a minimal overhead.

Unsupervised learning for signal mapping in dynamic photon emission

Dynamic photon emission is an efficient tool for timing analysis of various areas. However, advances in transistors integration bring more complex test patterns and more objects to investigate. As a consequence, understanding the analyzed area and finding nodes of interest can be difficult. In this paper, a method for drawing synthesis of the various signals met inside an area is reported. It is based on unsupervised learning tool for dimension reduction and clustering. The process is applied to real data to show its efficiency and its quality is evaluated.